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About This Role
Technical Lead – Applied AI/ML Projects
Want to build AI that does real work, not just demos well? Come lead teams designing LLM and agent-based solutions that ship inside real products for scientific organizations, from bench-science tooling to instrument software, with an emphasis on life sciences and healthcare.
About Yahara:
We help scientific organizations solve hard problems and move their industries forward, from life sciences startups and CROs to the teams building lab software and scientific instruments. It's work we're proud of, and we're excited about the successes still ahead.
Join a team recognized as one of Madison Magazine's Best Places to Work, where innovation thrives and collaboration drives success. We don't just build software. We empower people and transform industries.
Important Notes about this Position:
- This is a hybrid position based out of our office in Madison, WI.
- We are unable to provide sponsorship.
The Opportunity:
You’ll lead client projects where AI is a core part of the product. That means going past calling an API: designing retrieval, orchestrating agents, fine-tuning models when it’s worth it, and proving the whole thing works. You’ll set the technical direction, grow the engineers around you, and help clients figure out where AI genuinely fits.
Our Approach:
We build on strong engineering fundamentals: solid methodology, rigorous testing, and sound architecture. We use AI tools like Claude and Codex where they speed up delivery, while keeping critical thinking and engineering discipline at the center. We own the quality, accuracy, and understanding of the code we ship.
What You’ll Do:
- Design AI-powered features and workflows that solve real client problems.
- Build retrieval-augmented generation (RAG) systems over client data.
- Orchestrate agents and tool use for multi-step workflows.
- Fine-tune or adapt models when off-the-shelf performance falls short.
- Build evaluation suites that show whether the AI is actually getting it right.
- Put guardrails in place for accuracy, safety, privacy, and cost.
- Help clients separate problems AI solves well from ones it doesn’t.
- Own technical delivery for your project team: quality, pace, and direction.
- Mentor the engineers on your team through code reviews and one-on-one problem solving.
- Help engineers work through hard problems instead of solving them for them.
- Present results, tradeoffs, and risks to clients in plain language.
- Join pre-sales conversations with solution outlines and rough estimates.
- Document key decisions in ADRs so the next team knows why, not just what.
What You’ll Bring:
Experience:
- 6+ years in software engineering, with recent hands-on AI work.
- Experience shipping LLM-based features or applications to real users.
- Time leading technical work for a team, formally or informally.
- A solid grasp of how models are trained, tuned, and evaluated.
Mindset:
- Healthy skepticism about AI hype, paired with real enthusiasm for what works.
- Comfort making sound calls in a field that changes monthly.
- Genuine interest in helping other engineers grow.
- Ability to explain AI tradeoffs to non-technical stakeholders.
Nice to Have:
- Experience with life sciences organizations, lab systems, or regulated scientific software
- AI in regulated environments (HIPAA, FDA, GxP).
- Traditional ML or data science experience.
- Consulting or client-facing project experience.
- Cloud AI certifications (Azure or AWS).
Tech Like What You’ll Use (examples, not requirements; we’re stack agnostic):
- Python and/or TypeScript
- Model providers like Anthropic, OpenAI, or open-weight models
- Frameworks like LangChain, LlamaIndex, or the Model Context Protocol (MCP)
- Vector stores like pgvector, Pinecone, or Azure AI Search
- Cloud AI platforms like Azure AI Foundry or Amazon Bedrock
Company Benefits & Perks
- 20+ days of PTO accruable in the first year!
- Comprehensive health insurance (Medical, Dental, Vision) with HMO and PPO options
- Health Savings Account (HSA) with annual employer contributions
- 401(k) with guaranteed company match (Traditional and Roth options)
- 100% company-paid short-term and long-term disability
- 100% company-paid life insurance with option to increase coverage
- 100% company-paid identity theft protection
- On-site gym with basketball court
- Hybrid/remote schedule with home office stipend
- Fresh fruit, healthy snacks, and beverages provided daily
- Bonus certification program (Microsoft, AWS, PMP, IIBA, etc.)
- Employee Assistance Program (counseling, legal, financial services)
- Monthly and Quarterly Recognition Awards with spot bonuses
- Company-supported community outreach and volunteer opportunities
- Employee-run committee involvement opportunities
- Collaborative culture founded on realized values and incredible people
If you need an accommodation as part of the employment process, please contact Human Resources via email at [email protected]
Yahara Software LLC is an Equal Employment Opportunity/Affirmative Action Employer.
This is a full-time, salaried position with competitive salary and benefits. Candidates must be eligible to work in the U.S. on a permanent basis and can work on-site in our office located in Madison, Wisconsin.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Yahara Software, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Yahara Software AI Hiring
Yahara Software has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Madison, WI, US.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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